Metabolic targeting of hypoxia and HIF1 in solid tumors can enhance cytotoxic chemotherapy
Bibliographic record
Abstract
Solid tumors frequently contain large regions with low oxygen concentrations (hypoxia). The hypoxic microenvironment induces adaptive changes to tumor cell metabolism, and this alteration can further distort the local microenvironment. The net result of these tumor-specific changes is a microenvironment that inhibits many standard cytotoxic anticancer therapies and predicts for a poor clinical outcome. Pharmacologic targeting of the unique metabolism of solid tumors could alter the tumor microenvironment to provide more favorable conditions for anti-tumor therapy. Here, we describe a strategy in which the mitochondrial metabolism of tumor cells is increased by pharmacologic inhibition of hypoxia-inducible factor 1 (HIF1) or its target gene pyruvate dehydrogenase kinase 1 (PDK1). This acute increase in oxygen consumption leads to a corresponding decrease in tumor oxygenation. Whereas decreased oxygenation could reduce the effectiveness of some traditional therapies, we show that it dramatically increases the effectiveness of a hypoxia-specific cytotoxin. This treatment strategy should provide a high degree of tumor specificity for increasing the effectiveness of hypoxic cytotoxins, as it depends on the activation of HIF1 and the presence of hypoxia, conditions that are present only in the tumor, and not the normal tissue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".